# Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise

arXiv: https://arxiv.org/abs/2507.12492 · HTML: https://mlsanity.com/paper/2507.12492/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization](https://mlsanity.com/paper/2110.11331/) (2021)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption](https://mlsanity.com/paper/2412.01858/) (2024)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis](https://mlsanity.com/paper/2209.11514/) (2022)
- [False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2411.01313/) (2024)
- [Personalized Quantum Federated Learning for Privacy Image Classification](https://mlsanity.com/paper/2410.02547/) (2024)
- [Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms](https://mlsanity.com/paper/2412.05583/) (2024)
- [Performance Evaluation of LoRa Technology for Rural Connectivity: An Experimental Analysis in Nepal](https://mlsanity.com/paper/2412.04563/) (2024)
- [Microcontroller-Driven MPPT System for Enhanced Photovoltaic Efficiency: An Experimental Approach in Nepal](https://mlsanity.com/paper/2412.06956/) (2024)

## Built on by (most cited first, 8 of 8)

- [Quantum Federated Learning: A Comprehensive Survey](https://mlsanity.com/paper/2508.15998/) (2025)
- [Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus](https://mlsanity.com/paper/2510.06228/) (2025)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)
- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Things](https://mlsanity.com/paper/2511.22861/) (2025)
- [Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions](https://mlsanity.com/paper/2511.22148/) (2025)
- [Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach](https://mlsanity.com/paper/2601.07882/) (2026)

## Abstract (arXiv)

Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.